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To remove client names from a draft with local AI, run a local PII (personally identifiable information) detector over a copy of the text, check every name and identifier it flags, replace or delete the ones the AI task does not need, and read the result before it goes to any model. Automated detection narrows the work; it does not guarantee that every client name is found.
Why detection and transformation are two separate steps
Presidio, an open-source PII toolkit, splits the job into two stages. Its Analyzer identifies potential personal information in text, and its Anonymizer applies a chosen operation to the spans the Analyzer found. Keeping these apart matters because the detection step tells you what it thinks is sensitive, and the transformation step is where you decide what the text should look like afterwards.
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The Analyzer recognizes entities through several methods: named-entity recognition, regular expressions, deny lists, checksums, rules, and context words. Names are usually caught by named-entity recognition, while client account numbers or project codes often need a regular expression or a deny list you supply yourself. Presidio describes both predefined and custom recognizers, so a firm can add its own client list and internal code formats.
What “local” does and does not protect
Running the model on your own machine reduces one exposure, but it does not settle every privacy question about the application you use. Check the product-specific statements rather than assuming them from the word “local.”
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- Ollama states in its privacy policy (last updated March 2026) that the software runs on the local device and that prompts and responses are not used to train models. The same policy describes collecting device and usage information, so a local runtime is not automatically a no-collection runtime.
- Foundry Local, as described in Microsoft’s Windows AI FAQ, does not send input data to Microsoft servers. That statement applies to Foundry Local only and says nothing about other local AI tools.
Before you rely on any tool with a client document, read its current privacy policy, check its network settings, and look for plugins or logging features that could store prompts.
Step-by-step workflow
- Work on a copy. Duplicate the draft and keep the original in your normal secure storage. Do not run detection on the only copy.
- Run the Analyzer. Pass the text to the Presidio Analyzer, which returns each detected entity with its type, its position in the text, and a confidence score. Save this list; you will review it in the next step.
- Review the entity list. Go through every item. Remove false positives, such as a company name that is also a common word, and search the full text for client names, nicknames, abbreviations, project titles, and matter numbers the detector did not flag. Add a custom recognizer or a controlled deny list for any identifier that recurs across your documents.
- Choose an operator for each entity type. Use the table below. Apply the same treatment to every mention of the same client so the draft stays internally consistent.
- Run the Anonymizer. Apply the chosen operators to the flagged spans and save the output as a new file.
- Read the processed draft. Look for indirect identifiers: a unique job title, a city plus a date, a distinctive project description, or a sentence that only one client could match. Presidio’s documentation does not claim to remove re-identification risk from this kind of context, so this read is your responsibility.
- Verify the application before sending. Confirm the model runs locally in the way you intend, and that the application’s settings match its privacy statement.
Choosing how to replace each name
The operator you pick depends on what the AI task needs. Presidio’s Anonymizer supports the operators below. Only some of them can be reversed, and reversible methods bring their own key-management obligations.
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| Operator | What it does to the detected span | When it fits | Trade-off |
|---|---|---|---|
| Redact | Removes the span entirely | The name has no role in the task, such as summarizing a process or checking tone | Loses who did what, so the draft may stop making sense |
| Replace | Substitutes a placeholder such as [CLIENT_1] or [CLIENT_2] |
The AI needs to track roles, relationships, or repeated references | Every repeated mention must map to the same placeholder, or the text becomes confusing |
| Mask | Replaces some or all characters with a symbol such as * |
Partial concealment is enough for an internal review | Fragments of the original can remain recognizable |
| Hash | Replaces the value with a hash | You need a repeatable token that matches across documents | A hash is not the same as removing identifying risk, because the surrounding text can still identify the client |
| Encrypt | Replaces the value with an encrypted form | You must restore the original names later | Restoration requires the key to be stored and protected separately from the draft |
For most client drafts, replacement with stable placeholders is the safest default. It keeps the structure of the text, and because no key or mapping needs to exist outside your working file, nothing reversible is left lying around. Redaction is the better choice when the name serves no purpose at all.
Setting up Presidio
Presidio’s installation documentation describes installing the presidio-analyzer and presidio-anonymizer packages with pip, together with an NLP engine, and deploying the services with Docker. The documented supported Python versions are 3.10, 3.11, 3.12, and 3.13. The project’s guidance for production use is to pin an explicit container release tag rather than a floating tag, so that an update does not change detection behaviour mid-project.
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- AI NPU with XDNA 2 ARCHITECTURE - Powered by 16 “Zen 5” CPU cores, 50+ peak AI TOPS XDNA 2 NPU and a truly massive integrated GPU driven by 40 AMD RDNA 3.5 CUs, the Ryzen AI MAX+ 395 is a transformative upgrade and delivers a significant performance boost over the competition. The Ryzen AI Max+ 395 excels in consumer AI workloads like the llama.cpp-powered application: LM Studio. Shaping up to be the must-have app for client LLM workloads, LM Studio allows users to locally run the latest language model without any technical knowledge required and unleash their creativity and productivity.
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The project overview states that Presidio is transitioning to community ownership. Because project structure and package locations can change in that transition, use the current documentation and package registry entries as the authority for install commands, and do not rely on older tutorials.
Checks before the draft leaves your machine
- The Analyzer output has been reviewed line by line, and every client name appears only in its replaced or redacted form.
- Repeated mentions of the same client map to the same placeholder.
- Matter numbers, project codes, and email or account identifiers have been handled, not only personal names.
- The processed text has been read in full for indirect identifiers.
- The application you are using is the one whose privacy statement you checked, and its network and logging settings match that statement.
- The original, unprocessed draft is stored only where your firm’s rules allow.
What automated detection will miss
Detection tools are configured and tested against general patterns, not against your client list. A short name, a nickname that appears only once, an unusual spelling, or a client identified only by a description of their business can pass through unflagged. Documentation for Presidio describes its recognizers and configuration; it does not state a recall figure for names, aliases, or document context, and no published accuracy number for your documents should be assumed. Your review step is what closes that gap, and it is the part of this workflow that cannot be automated away.
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